Reservoir computing describes a broad range of recurrent neural networks, including liquid state machines and echo state networks. Reservoir computing uses a collection of recurrently connected units called a reservoir. Inputs are accepted by the reservoir and mapped to a higher dimension. The state of the reservoir can then be read to determine the desired output. Reservoir computing offers the potential for efficient parallel processing and nonlinear signal discrimination. For example, reservoir computing can be used to efficiently solve a number of tasks that are deemed computationally difficult, such as identifying features images, predicting chaotic time series, and speech recognition.
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